ABSTRACT Over the past decade, as demand for cloud services has surged, the strategic selection of these services has become increasingly crucial. The growing complexity within the cloud industry underscores the urgent need for a robust model for choosing cloud services effectively. Users often struggle to make informed decisions due to the dynamic nature and varying quality of available cloud services. In response, this paper introduces a novel decision‐making approach aimed at optimizing the selection process by identifying the most suitable combination of cloud services. The focus is on integrating these services into a cohesive ensemble to better fulfill user requirements. In contrast to existing methodologies, our approach evaluates cloud services on a continuous scale, taking into account critical tasks such as workload balancing, storage management, and network resource handling. We propose a model for selecting optimal composite cloud services, which includes real‐time optimization and addresses the consideration of null values for Quality of Service (QoS)‐based attributes (e.g., response time, cost, availability, and reliability) in the dataset—a factor overlooked by current literature. The proposed algorithm is inspired by computational intelligence and driven by an evolutionary algorithm‐based approach that undergoes evaluation across multiple datasets. The results illustrate its superiority, showcasing its ability to outperform existing optimization‐based methods in terms of execution time.

The integration of emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, and blockchain is transforming the landscape of modern healthcare. These technologies enable real-time monitoring, data-driven diagnostics, personalized treatment, and remote patient care, leading to more efficient and accessible healthcare delivery. This paper presents a comprehensive review of smart and advanced healthcare solutions driven by these technologies, exploring their architecture, applications, benefits, and limitations. It categorizes healthcare use cases into critical domains, including wearable health devices, intelligent diagnostics, telemedicine, and emergency response systems. Furthermore, the paper critically examines key challenges such as interoperability, energy efficiency, data quality, security, and ethical concerns. To address these issues, it discusses current solutions and highlights future research directions essential for scalable and sustainable healthcare innovation.

In today's interconnected society, ensuring the safety and security of the public is crucial for protecting individuals from threats posed by criminals. Equally important is safeguarding individuals' privacy, which requires the development of a robust mechanism capable of withstanding attacks while remaining efficient. Surveillance cameras, such as Closed-Circuit Television (CCTV), have become essential tools in this endeavour. These cameras capture extensive visual data, often processed and stored on remote, centralized cloud servers. However, these methods have several shortcomings and are vulnerable to numerous security and privacy breaches. Various solutions have been proposed to address these challenges, each focusing solely on specific aspects of the system. However, none of them have comprehensively addressed the complete mechanism and discussed its resilience against various cyber threats. This paper proposes a secure and trusted decentralized surveillance mechanism utilizing hashgraph for faster, scalable, and secure consensus. The framework aims to defend against attacks such as DDoS and Sybil Attacks and ensures tamperproof logging of all events securely. For rapid identification, we employ I-frame generation from camera footage and a two-stage detection process: one at the camera end using the MobileNetV3-small model to forward necessary keyframes, and another on a private cloud-based analyzer. To safeguard individuals' privacy and ensure secure I-frame transmission while preventing attacks such as Known-plaintext and Replay attacks, we implement an XORing mechanism alongside AES (Advanced Encryption Standard) for I-frames.

Industry 5.0 is the next stage in industrial evolution, collaborating between human ingenuity and intelligent technologies to provide manufacturing solutions. Integrating modern technology like Artificial Intelligence (AI), robotics, and the Internet of Things (IoT) into manufacturing and production processes characterizes Industry 5.0. On the other hand, edge computing provides real-time data processing and analysis at the networks edge, closer to the data source and a vital component of Industry 5.0. Edge computing enables Industry 5.0 to access and communicate information about their industrial sectors using more accessible, standard hardware and software resources. However, no recent survey papers have examined the importance of edge computing in Industry 5.0. This study aims to fill that gap by presenting a survey on the importance of edge computing in Industry 5.0 and discussing a variety of technologies that could be used to implement and support this new industrial paradigm. First, we outline an overview and fundamentals of edge computing in Industry 5.0 architecture. Then objectives of Industry 5.0 are summarized to address various research challenges, including privacy, human-robot co-working, sustainability, and robust networks. Afterwards, this paper provides an extensive overview of emerging technologies for Industry 5.0, such as collaborative robots, AI, Digital Twins, and many more. In addition, this survey highlights various open research challenges and potential solutions that should be addressed further to achieve Industry 5.0.

With the ever-increasing demand for Internet of Things (IoT) applications, wireless sensor networks (WSNs) have become the central means to disseminate data for analysis in the era of mobile edge computing. Mobile sinks (MSs) as edge nodes have emerged as an efficient solution to the performance enhancement of WSNs. One important task of the MSs is to collect data in a sustainable and optimized manner by visiting certain rendezvous points (RPs) inside the WSN. However, most existing works focus only on connected WSNs, while disjoint networks are the reality in many IoT applications. Moreover, none of them have considered a realistic propagation model. They have also ignored optimizing both the number of RPs and MSs. This paper proposes a novel data collection scheme while paying attention to all these issues. The scheme is specially designed for delay-harsh applications. First, we propose a convex hull-based algorithm to determine RPs for constructing an optimal tour of a MS. Then using the resulting set of RPs, we present another algorithm based on the Jaya metaheuristic to determine an optimal number of MSs and their balanced tours. Rigorous simulations show that our scheme outperforms existing algorithms in terms of various performance metrics.

Mobile chargers have greatly promoted the wireless rechargeable sensor networks (WRSNs). While most recent works have focused on recharging the WRSNs in an on-demand fashion, little attention has been paid on joint consideration of multiple mobile chargers (MCs) and multi-node energy transfer for determining the charging schedule of energy-hungry nodes. Moreover, most of the schemes leave out the contemplation of multiple network attributes while making scheduling decisions and even they overlook the issue of ill-timed charging response to the nodes with uneven energy consumption rates. In this paper, we address the aforesaid issues together and propose a novel scheduling scheme for on-demand charging in WRSNs. We first present an efficient network partitioning method for distributing the MCs so as to evenly balance their workload. We next adopt the fuzzy logic which blends various network attributes for determining the charging schedule of the MCs. We also formulate an expression to determine the charging threshold for the nodes that vary depending on their energy consumption rate. Extensive simulations are conducted to demonstrate the effectiveness and competitiveness of our scheme. The comparison results reveal that the proposed scheme improves charging performance compared to the state-of-the-art schemes with respect to various performance metrics.

Exploiting mobile sink (MS) for data gathering in the wireless sensor networks has been extensively studied in the recent researches to address energy‐hole issues, thereby facilitating balanced energy consumption among nodes and so prolonging network lifetime. However, such approaches suffer from an extended data collection delay causing buffer overflow problem. In this regard, finding the optimal number of locations (i.e. rendezvous points (RPs) where the MS sojourns for data collection), is not only of utmost importance, but also a challenging task. A novel scheme for trajectory design of MS for data collection is presented in this study. The authors' primary goal is to optimise the number of RPs and their locations to minimise the travelling length of the MS. First, they reduced the problem size by using a combination of breadth‐first search and Tarjan's algorithm and then applied spectral clustering to find the optimal set of RPs to plan the tour for the MS. They have performed extensive simulations, and the results are compared with relevant existing schemes. The comparative results confirm the effectiveness of their approach in terms of the number of RPs, path length, the variance of RPs, and energy consumption per round.